{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Bet sizing 1",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyM/5dxdR3vAkoLvqzTyRq3f",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jo-cho/advances_in_financial_machine_learning/blob/master/Bet%20Sizing/Bet_sizing_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5bthg3s23JVw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# lib\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import datetime as dt\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns; sns.set()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P_w5609_w735",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# sklearn\n",
        "\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier, BaggingClassifier\n",
        "from sklearn.metrics import roc_curve, confusion_matrix, accuracy_score, classification_report\n",
        "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
        "from sklearn import metrics"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lefH2Ji6WyLw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(action='ignore')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KvpQ32OS2joc",
        "colab_type": "code",
        "outputId": "f751e3a0-38b4-49f4-c871-eeddca6ffeab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J5F9VG8B2oB0",
        "colab_type": "code",
        "outputId": "a5c25a1b-922f-438c-9bc0-bfd3d33a47d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Thanks to mlfinlab!\n",
        "\n",
        "!pip install mlfinlab\n",
        "from mlfinlab import data_structures, features, filters, labeling, util, sampling"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: mlfinlab in /usr/local/lib/python3.6/dist-packages (0.5.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "twYwXobOVmCG",
        "colab_type": "text"
      },
      "source": [
        "## META-LABELING"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NeFHQgICirXE",
        "colab_type": "text"
      },
      "source": [
        "### What is meta labeling?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sEmeWgLwuKo_",
        "colab_type": "text"
      },
      "source": [
        "The first step is to train a primary model (binary classification). \n",
        "\n",
        "Second a threshold level is determined at which the primary model has a high recall, in the coded example you will find that 0.30 is a good threshold, ROC curves could be used to help determine a good level. \n",
        "\n",
        "Third the features from the first model are concatenated with the predictions from the first model, into a new feature set for the secondary model. Meta Labels are used as the target variable in the second model. \n",
        "Now fit the second model. \n",
        "\n",
        "Fourth the prediction from the secondary model is combined with the prediction from the primary model and only where both are true, is your final prediction true. I.e. if your primary model predicts a 3 and your secondary model says you have a high probability of the primary model being correct, is your final prediction a 3, else not 3."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XYj34cC_8aR4",
        "colab_type": "text"
      },
      "source": [
        "Binary classification problems present a trade-off between type-I errors (false positives) and type-II errors (false negatives). In general, increasing the true positive rate of a binary classifier will tend to increase its false positive rate. The receiver operating characteristic (ROC) curve of a binary classifier measures the cost of increasing the true positive rate, in terms of accepting higher false positive rates.\n",
        "\n",
        "Meta-labeling is particularly helpful when you want to achieve higher F1-scores. First, we build a model that achieves high recall, even if the precision is not particularly high. Second, we correct for the low precision by applying meta-labeling to the positives predicted by the primary model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lpnKi3rNfYf4",
        "colab_type": "code",
        "outputId": "8a50789a-4dcc-486a-8b3b-13e8414179f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        }
      },
      "source": [
        "from IPython.core.display import Image\n",
        "Image(filename='/content/drive/My Drive/Colab Notebooks/images/meta-labeling.png')"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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HZcrU3XL2XIq6n5GRJ8XFJU+qpcQTUjESP8JJ+7itlH24m8R31FpkGSJFiafEU+IJJd5W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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PsrMWBgNhBgE",
        "colab_type": "text"
      },
      "source": [
        "### Read in data - dollar bars"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lypoa5PW2xIg",
        "colab_type": "code",
        "outputId": "5bffcd3d-5906-4607-f8d9-ecfd5cb36bba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "# make dollar bars from tick data\n",
        "raw_dollar_bars = data_structures.get_dollar_bars('/content/drive/My Drive/Colab Notebooks/csv/clean_IVE_tickbidask2.csv', threshold=1_000_000)\n",
        "dollar_bars = raw_dollar_bars.set_index(pd.to_datetime(raw_dollar_bars.date_time))\n",
        "dollar_bars = dollar_bars.drop(columns='date_time')\n",
        "dollar_bars = dollar_bars.reset_index().drop_duplicates(subset='date_time', keep='last').set_index('date_time')"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading data in batches:\n",
            "Batch number: 0\n",
            "Returning bars \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6J1aOceRiGr7",
        "colab_type": "code",
        "outputId": "7881e7e3-7e56-4a56-bdd5-e4557614acc0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "dollar_bars.head()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tick_num</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>cum_buy_volume</th>\n",
              "      <th>cum_ticks</th>\n",
              "      <th>cum_dollar_value</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date_time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2009-09-28 09:46:35</th>\n",
              "      <td>62</td>\n",
              "      <td>50.79</td>\n",
              "      <td>51.07</td>\n",
              "      <td>50.710</td>\n",
              "      <td>51.07</td>\n",
              "      <td>19844</td>\n",
              "      <td>13264</td>\n",
              "      <td>62</td>\n",
              "      <td>1.010425e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-09-28 09:53:49</th>\n",
              "      <td>108</td>\n",
              "      <td>51.07</td>\n",
              "      <td>51.15</td>\n",
              "      <td>51.050</td>\n",
              "      <td>51.14</td>\n",
              "      <td>20324</td>\n",
              "      <td>16274</td>\n",
              "      <td>46</td>\n",
              "      <td>1.038593e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-09-28 09:55:26</th>\n",
              "      <td>143</td>\n",
              "      <td>51.14</td>\n",
              "      <td>51.15</td>\n",
              "      <td>51.129</td>\n",
              "      <td>51.14</td>\n",
              "      <td>19622</td>\n",
              "      <td>13098</td>\n",
              "      <td>35</td>\n",
              "      <td>1.003401e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-09-28 10:02:52</th>\n",
              "      <td>201</td>\n",
              "      <td>51.14</td>\n",
              "      <td>51.26</td>\n",
              "      <td>51.080</td>\n",
              "      <td>51.25</td>\n",
              "      <td>22319</td>\n",
              "      <td>17454</td>\n",
              "      <td>58</td>\n",
              "      <td>1.142880e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-09-28 10:10:21</th>\n",
              "      <td>254</td>\n",
              "      <td>51.25</td>\n",
              "      <td>51.29</td>\n",
              "      <td>51.190</td>\n",
              "      <td>51.29</td>\n",
              "      <td>21519</td>\n",
              "      <td>14254</td>\n",
              "      <td>53</td>\n",
              "      <td>1.103149e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     tick_num   open  ...  cum_ticks  cum_dollar_value\n",
              "date_time                             ...                             \n",
              "2009-09-28 09:46:35        62  50.79  ...         62      1.010425e+06\n",
              "2009-09-28 09:53:49       108  51.07  ...         46      1.038593e+06\n",
              "2009-09-28 09:55:26       143  51.14  ...         35      1.003401e+06\n",
              "2009-09-28 10:02:52       201  51.14  ...         58      1.142880e+06\n",
              "2009-09-28 10:10:21       254  51.25  ...         53      1.103149e+06\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oYC5u9X5jqc5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "close = dollar_bars.close"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aovQU2FLhcYp",
        "colab_type": "text"
      },
      "source": [
        "### Primary model (side) \n",
        "- Based on the simple moving average cross-over strategy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RVZiwS-dPhzs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fast_window = 20\n",
        "slow_window = 50\n",
        "\n",
        "df = pd.DataFrame()\n",
        "df['fast_mavg'] = close.rolling(window=fast_window, min_periods=fast_window, center=False).mean()\n",
        "df['slow_mavg'] = close.rolling(window=slow_window, min_periods=slow_window, center=False).mean()\n",
        "df['side'] = np.nan\n",
        "\n",
        "long_signals = df['fast_mavg'] >= df['slow_mavg'] \n",
        "short_signals = df['fast_mavg'] < df['slow_mavg'] \n",
        "df.loc[long_signals, 'side'] = 1\n",
        "df.loc[short_signals, 'side'] = -1\n",
        "\n",
        "df['side'] = df['side'].shift(1)\n",
        "\n",
        "side = df['side']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gDT_ozxvj7eU",
        "colab_type": "code",
        "outputId": "890ae9fa-0e63-49e2-edf0-e139bcdf26b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "side.value_counts()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " 1.0    32190\n",
              "-1.0    26367\n",
              "Name: side, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pT2NSRXx24iv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# form a tripple-barrier\n",
        "pt_sl = [1,2]\n",
        "\n",
        "target = util.get_daily_vol(close,lookback=50)\n",
        "\n",
        "h = target.mean()*0.5\n",
        "t_events = filters.cusum_filter(close, h) # using symmetric cusum filter get t_events\n",
        "\n",
        "vertical_barrier_times = labeling.add_vertical_barrier(t_events, close, num_days=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uAn2kWFFWZZv",
        "colab_type": "code",
        "outputId": "10b9c3b2-f375-42b0-9f9d-3a285ffef057",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# events\n",
        "min_ret = 0.005\n",
        "\n",
        "events = labeling.get_events(close, t_events, pt_sl, target, min_ret, num_threads=3, \n",
        "                             vertical_barrier_times = vertical_barrier_times ,side_prediction = side)\n",
        "\n",
        "# label (bin)\n",
        "\n",
        "bin = labeling.get_bins(events, close)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-01-24 08:20:37.773005 100.0% apply_pt_sl_on_t1 done after 0.22 minutes. Remaining 0.0 minutes.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p-p8yHmHW0M7",
        "colab_type": "code",
        "outputId": "9125f719-3a7b-4332-f10e-27ba7ca39535",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        }
      },
      "source": [
        "bin.describe()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ret</th>\n",
              "      <th>trgt</th>\n",
              "      <th>bin</th>\n",
              "      <th>side</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6404.000000</td>\n",
              "      <td>6410.000000</td>\n",
              "      <td>6410.000000</td>\n",
              "      <td>6404.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-0.000117</td>\n",
              "      <td>0.009983</td>\n",
              "      <td>0.312012</td>\n",
              "      <td>-0.059963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.013082</td>\n",
              "      <td>0.006454</td>\n",
              "      <td>0.463351</td>\n",
              "      <td>0.998279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-0.237752</td>\n",
              "      <td>0.005000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-0.007115</td>\n",
              "      <td>0.006446</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.001033</td>\n",
              "      <td>0.008225</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.007684</td>\n",
              "      <td>0.011075</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.373211</td>\n",
              "      <td>0.094337</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               ret         trgt          bin         side\n",
              "count  6404.000000  6410.000000  6410.000000  6404.000000\n",
              "mean     -0.000117     0.009983     0.312012    -0.059963\n",
              "std       0.013082     0.006454     0.463351     0.998279\n",
              "min      -0.237752     0.005000     0.000000    -1.000000\n",
              "25%      -0.007115     0.006446     0.000000    -1.000000\n",
              "50%       0.001033     0.008225     0.000000    -1.000000\n",
              "75%       0.007684     0.011075     1.000000     1.000000\n",
              "max       0.373211     0.094337     1.000000     1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-vX3SkcXYOF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bin.dropna(inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i2chOOmHXUAZ",
        "colab_type": "code",
        "outputId": "8556d9cf-7256-4d55-9281-c2654e565d70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "bin.side.value_counts()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-1.0    3394\n",
              " 1.0    3010\n",
              "Name: side, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rWR7vJWEWnJX",
        "colab_type": "code",
        "outputId": "b059c0d9-07bf-4a26-c467-2c1b7245a8ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "bin.bin.value_counts()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    4404\n",
              "1    2000\n",
              "Name: bin, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wfxFGLbAqC-t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "raw_data = pd.DataFrame(close)\n",
        "\n",
        "log_ret = np.log(raw_data['close']).diff()\n",
        "\n",
        "# Momentum\n",
        "raw_data['mom1'] = raw_data['close'].pct_change(periods=1)\n",
        "raw_data['mom2'] = raw_data['close'].pct_change(periods=2)\n",
        "raw_data['mom3'] = raw_data['close'].pct_change(periods=3)\n",
        "\n",
        "# Volatility\n",
        "raw_data['volatility_50'] = log_ret.rolling(window=50, min_periods=50, center=False).std()\n",
        "raw_data['volatility_31'] = log_ret.rolling(window=31, min_periods=31, center=False).std()\n",
        "raw_data['volatility_15'] = log_ret.rolling(window=15, min_periods=15, center=False).std()\n",
        "\n",
        "# Serial Correlation\n",
        "window_autocorr = 50\n",
        "\n",
        "raw_data['autocorr_1'] = log_ret.rolling(window=window_autocorr, min_periods=window_autocorr, center=False).apply(lambda x: x.autocorr(lag=1), raw=False)\n",
        "raw_data['autocorr_2'] = log_ret.rolling(window=window_autocorr, min_periods=window_autocorr, center=False).apply(lambda x: x.autocorr(lag=2), raw=False)\n",
        "raw_data['autocorr_3'] = log_ret.rolling(window=window_autocorr, min_periods=window_autocorr, center=False).apply(lambda x: x.autocorr(lag=3), raw=False)\n",
        "\n",
        "# Remove look ahead bias\n",
        "raw_data = raw_data.shift(1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jRbf20dNtVlS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "outputId": "ec4f4cd7-e009-4c9a-f927-a27ac59f4fde"
      },
      "source": [
        "raw_X = raw_data.iloc[:,1:]\n",
        "raw_X.dropna(inplace=True)\n",
        "raw_X.head()"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mom1</th>\n",
              "      <th>mom2</th>\n",
              "      <th>mom3</th>\n",
              "      <th>volatility_50</th>\n",
              "      <th>volatility_31</th>\n",
              "      <th>volatility_15</th>\n",
              "      <th>autocorr_1</th>\n",
              "      <th>autocorr_2</th>\n",
              "      <th>autocorr_3</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date_time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2009-09-30 15:42:01</th>\n",
              "      <td>-0.005646</td>\n",
              "      <td>-0.006353</td>\n",
              "      <td>-0.007579</td>\n",
              "      <td>0.002705</td>\n",
              "      <td>0.003341</td>\n",
              "      <td>0.003551</td>\n",
              "      <td>-0.051927</td>\n",
              "      <td>0.216036</td>\n",
              "      <td>-0.302316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-09-30 15:58:56</th>\n",
              "      <td>0.001371</td>\n",
              "      <td>-0.004283</td>\n",
              "      <td>-0.004991</td>\n",
              "      <td>0.002705</td>\n",
              "      <td>0.003353</td>\n",
              "      <td>0.003594</td>\n",
              "      <td>-0.071628</td>\n",
              "      <td>0.205664</td>\n",
              "      <td>-0.310500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 09:30:07</th>\n",
              "      <td>0.000391</td>\n",
              "      <td>0.001762</td>\n",
              "      <td>-0.003894</td>\n",
              "      <td>0.002706</td>\n",
              "      <td>0.003347</td>\n",
              "      <td>0.003583</td>\n",
              "      <td>-0.070389</td>\n",
              "      <td>0.189256</td>\n",
              "      <td>-0.312039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 09:39:37</th>\n",
              "      <td>-0.003518</td>\n",
              "      <td>-0.003129</td>\n",
              "      <td>-0.001762</td>\n",
              "      <td>0.002733</td>\n",
              "      <td>0.003394</td>\n",
              "      <td>0.003663</td>\n",
              "      <td>-0.078848</td>\n",
              "      <td>0.180343</td>\n",
              "      <td>-0.242727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 09:52:26</th>\n",
              "      <td>-0.002942</td>\n",
              "      <td>-0.006450</td>\n",
              "      <td>-0.006062</td>\n",
              "      <td>0.002759</td>\n",
              "      <td>0.003416</td>\n",
              "      <td>0.003565</td>\n",
              "      <td>-0.048314</td>\n",
              "      <td>0.173996</td>\n",
              "      <td>-0.255830</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         mom1      mom2  ...  autocorr_2  autocorr_3\n",
              "date_time                                ...                        \n",
              "2009-09-30 15:42:01 -0.005646 -0.006353  ...    0.216036   -0.302316\n",
              "2009-09-30 15:58:56  0.001371 -0.004283  ...    0.205664   -0.310500\n",
              "2009-10-01 09:30:07  0.000391  0.001762  ...    0.189256   -0.312039\n",
              "2009-10-01 09:39:37 -0.003518 -0.003129  ...    0.180343   -0.242727\n",
              "2009-10-01 09:52:26 -0.002942 -0.006450  ...    0.173996   -0.255830\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2YTsGoXDwAK6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "outputId": "2e77ca05-a649-42ee-8a4e-8d4be339373f"
      },
      "source": [
        "# Feature decomposition PCA\n",
        "\n",
        "n=5\n",
        "pca = PCA(n)\n",
        "pca.fit(raw_X)\n",
        "pcaX = pca.transform(raw_X)\n",
        "\n",
        "X = pd.DataFrame(index=raw_X.index)\n",
        "for i in range(n):\n",
        "  a = pd.Series(data=pcaX[:,i],index=raw_X.index,name=i)\n",
        "  X = X.join(a)\n",
        "\n",
        "X['side'] = side\n",
        "\n",
        "X.head()"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>side</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date_time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2009-09-30 15:42:01</th>\n",
              "      <td>0.093435</td>\n",
              "      <td>-0.355411</td>\n",
              "      <td>-0.010021</td>\n",
              "      <td>0.011007</td>\n",
              "      <td>0.003954</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-09-30 15:58:56</th>\n",
              "      <td>0.103315</td>\n",
              "      <td>-0.353263</td>\n",
              "      <td>-0.031490</td>\n",
              "      <td>0.005555</td>\n",
              "      <td>0.003636</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 09:30:07</th>\n",
              "      <td>0.093653</td>\n",
              "      <td>-0.345701</td>\n",
              "      <td>-0.042566</td>\n",
              "      <td>0.001394</td>\n",
              "      <td>0.003241</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 09:39:37</th>\n",
              "      <td>0.108490</td>\n",
              "      <td>-0.282626</td>\n",
              "      <td>-0.015056</td>\n",
              "      <td>0.004202</td>\n",
              "      <td>0.003426</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-01 09:52:26</th>\n",
              "      <td>0.077217</td>\n",
              "      <td>-0.294765</td>\n",
              "      <td>-0.010693</td>\n",
              "      <td>0.009079</td>\n",
              "      <td>0.003859</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            0         1         2         3         4  side\n",
              "date_time                                                                  \n",
              "2009-09-30 15:42:01  0.093435 -0.355411 -0.010021  0.011007  0.003954  -1.0\n",
              "2009-09-30 15:58:56  0.103315 -0.353263 -0.031490  0.005555  0.003636  -1.0\n",
              "2009-10-01 09:30:07  0.093653 -0.345701 -0.042566  0.001394  0.003241  -1.0\n",
              "2009-10-01 09:39:37  0.108490 -0.282626 -0.015056  0.004202  0.003426  -1.0\n",
              "2009-10-01 09:52:26  0.077217 -0.294765 -0.010693  0.009079  0.003859  -1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3PBgcFJGxlI-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c7729375-f2f1-4808-a404-f52fb67a5ddf"
      },
      "source": [
        "len(X)"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "58556"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0BgIS4xtjZt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tmp_data = X.copy()\n",
        "tmp_data = tmp_data.join([bin.bin, events['t1']])\n",
        "tmp_data.dropna(inplace=True)\n",
        "\n",
        "X = tmp_data.iloc[:,:6]\n",
        "y = tmp_data[['bin']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QtC_iYLvyHSz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, shuffle=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eoDiKFRqz5Dd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "5928653a-bbd0-40d9-b98d-55ed769f91ae"
      },
      "source": [
        "#avgU = sampling.get_av_uniqueness_from_triple_barrier(events, close, 3).mean()\n",
        "#max_samples = avgU[0]"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-01-24 09:28:13.797329 100.0% num_concurrent_events done after 0.08 minutes. Remaining 0.0 minutes.\n",
            "2020-01-24 09:28:16.928423 100.0% _get_average_uniqueness done after 0.05 minutes. Remaining 0.0 minutes.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I2Kd2E7UvOtz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#clf0 = RandomForestClassifier(n_estimators=1,criterion='entropy',bootstrap = False, class_weight='balanced_subsample')\n",
        "#clf1 = BaggingClassifier(base_estimator=clf0, max_samples=max_samples, max_features=1, n_estimators=1000)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TDGjgdtI16yI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "outputId": "c1874567-89be-4d4c-b32a-6147964d559b"
      },
      "source": [
        "#clf1.fit(X_train, y_train.values.ravel())"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BaggingClassifier(base_estimator=RandomForestClassifier(bootstrap=False,\n",
              "                                                        ccp_alpha=0.0,\n",
              "                                                        class_weight='balanced_subsample',\n",
              "                                                        criterion='entropy',\n",
              "                                                        max_depth=None,\n",
              "                                                        max_features='auto',\n",
              "                                                        max_leaf_nodes=None,\n",
              "                                                        max_samples=None,\n",
              "                                                        min_impurity_decrease=0.0,\n",
              "                                                        min_impurity_split=None,\n",
              "                                                        min_samples_leaf=1,\n",
              "                                                        min_samples_split=2,\n",
              "                                                        min_weight_fraction_leaf=0.0,\n",
              "                                                        n_estimators=1,\n",
              "                                                        n_jobs=None,\n",
              "                                                        oob_score=False,\n",
              "                                                        random_state=None,\n",
              "                                                        verbose=0,\n",
              "                                                        warm_start=False),\n",
              "                  bootstrap=True, bootstrap_features=False, max_features=1,\n",
              "                  max_samples=0.28313174769037236, n_estimators=1000,\n",
              "                  n_jobs=None, oob_score=False, random_state=None, verbose=0,\n",
              "                  warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zq1n0S81P4ue",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "parameters = {'max_depth':[2, 3, 4, 5, 7],\n",
        "              'n_estimators':[1, 10, 25, 50, 100, 256, 512],\n",
        "              'random_state':[42]}\n",
        "    \n",
        "rf = RandomForestClassifier(criterion='entropy', class_weight='balanced_subsample')\n",
        "    \n",
        "    clf = GridSearchCV(rf, parameters, cv=4, scoring='roc_auc', n_jobs=3)\n",
        "    \n",
        "    clf.fit(X_data, y_data)\n",
        "    \n",
        "    print(clf.cv_results_['mean_test_score'])\n",
        "    \n",
        "    return clf.best_params_['n_estimators'], clf.best_params_['max_depth']\n",
        "rf = RandomForestClassifier()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JE-cvkda5wXW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = clf1.predict(X_test)\n",
        "y_pred_prob = clf1.predict_proba(X_test)[:, 1] # 1로 예상할 확률"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UQKHmmsd0e3F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "56df9b93-6d51-4ee4-975b-7d87c54d1946"
      },
      "source": [
        "fpr_rf, tpr_rf, _ = roc_curve(y_test, y_pred_prob)\n",
        "\n",
        "plt.figure(1)\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.plot(fpr_rf, tpr_rf, label='RF')\n",
        "plt.xlabel('False positive rate')\n",
        "plt.ylabel('True positive rate')\n",
        "plt.title('ROC curve')\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BGxy/lKANBqeKlrSo44Rvc3fWr1nMiuWLGTBgMKNH/wdLy3JGrrhokpAQQpQYj7LURF1L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\nBwnGGGOiOEgwxhgTxUGCMcaYqD8AqcK46laRAyYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9dkg01-O5yWR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "outputId": "027d13ac-571c-47cb-9f2c-16e7f1ed650c"
      },
      "source": [
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "print(\"Accuracy = \", accuracy_score(y_test, y_pred))\n",
        "print('')\n",
        "print(\"Confusion Matrix\")\n",
        "mat = confusion_matrix(y_test, y_pred)\n",
        "sns.heatmap(mat , annot=True)\n",
        "plt.xlabel('predicted label')\n",
        "plt.ylabel('true label');"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.63      1.00      0.77      1616\n",
            "         1.0       0.40      0.00      0.01       946\n",
            "\n",
            "    accuracy                           0.63      2562\n",
            "   macro avg       0.52      0.50      0.39      2562\n",
            "weighted avg       0.55      0.63      0.49      2562\n",
            "\n",
            "Accuracy =  0.629976580796253\n",
            "\n",
            "Confusion Matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IyJ5Zo+d//vx5+Pj4oEuXLgCuT6j55ptvUFpaitzcXERGRgIAIiMjkZubi7KyMrNtTSUp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\nMsTwJyKSof8HND9YBwEGzFYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pUmsGwGV6euL",
        "colab_type": "text"
      },
      "source": [
        "## Bet Sizing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zUfHMzWr78ar",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mlfinlab import bet_sizing as bs"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HBRzgVhM6g5j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "p = y_pred_prob"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "75bIPHHR7UUo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "aebec2f9-6e12-4893-84f2-85bb2c418e61"
      },
      "source": [
        "new_data = y_test.copy()\n",
        "new_data['p'] = p\n",
        "new_data['pred'] = y_pred\n",
        "new_data"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bin</th>\n",
              "      <th>p</th>\n",
              "      <th>pred</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date_time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-07-09 09:56:21</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.289559</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-09 12:00:31</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.413559</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-09 15:59:28</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.253173</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-10 09:35:27</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.361351</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-10 11:48:00</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.254544</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-12 11:59:18</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.286559</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-13 09:36:29</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.369559</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-18 09:50:22</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.229559</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-18 12:22:51</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.289544</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-19 14:01:58</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.342270</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2562 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                     bin         p  pred\n",
              "date_time                               \n",
              "2015-07-09 09:56:21  0.0  0.289559   0.0\n",
              "2015-07-09 12:00:31  0.0  0.413559   0.0\n",
              "2015-07-09 15:59:28  1.0  0.253173   0.0\n",
              "2015-07-10 09:35:27  1.0  0.361351   0.0\n",
              "2015-07-10 11:48:00  1.0  0.254544   0.0\n",
              "...                  ...       ...   ...\n",
              "2019-06-12 11:59:18  0.0  0.286559   0.0\n",
              "2019-06-13 09:36:29  0.0  0.369559   0.0\n",
              "2019-06-18 09:50:22  0.0  0.229559   0.0\n",
              "2019-06-18 12:22:51  0.0  0.289544   0.0\n",
              "2019-06-19 14:01:58  1.0  0.342270   0.0\n",
              "\n",
              "[2562 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XcNyLMBr6yFU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# test에 맞추자\n",
        "new_events = events.loc[new_data.index]\n",
        "bs_df = pd.concat([new_events, new_data],axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lqyt-EMU8QZZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "ea81db17-accc-4a5f-8c1a-10d50c1339f5"
      },
      "source": [
        "bs_df"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>t1</th>\n",
              "      <th>trgt</th>\n",
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              "      <th>pt</th>\n",
              "      <th>sl</th>\n",
              "      <th>bin</th>\n",
              "      <th>p</th>\n",
              "      <th>pred</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date_time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-07-09 09:56:21</th>\n",
              "      <td>2015-07-10 09:56:51</td>\n",
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              "      <th>2015-07-09 12:00:31</th>\n",
              "      <td>2015-07-10 12:27:37</td>\n",
              "      <td>0.007981</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2015-07-09 15:59:28</th>\n",
              "      <td>2015-07-10 09:35:27</td>\n",
              "      <td>0.006833</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-10 09:35:27</th>\n",
              "      <td>2015-07-13 09:30:23</td>\n",
              "      <td>0.006412</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.361351</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2015-07-10 11:48:00</th>\n",
              "      <td>2015-07-13 09:30:23</td>\n",
              "      <td>0.005870</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-12 11:59:18</th>\n",
              "      <td>2019-06-13 12:16:14</td>\n",
              "      <td>0.005909</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.286559</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-13 09:36:29</th>\n",
              "      <td>2019-06-14 09:48:52</td>\n",
              "      <td>0.005255</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "      <td>0.369559</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-18 09:50:22</th>\n",
              "      <td>2019-06-19 09:57:29</td>\n",
              "      <td>0.005684</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>2019-06-18 12:22:51</th>\n",
              "      <td>2019-06-19 12:33:36</td>\n",
              "      <td>0.007223</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "      <td>0.289544</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-19 14:01:58</th>\n",
              "      <td>2019-06-20 09:30:00</td>\n",
              "      <td>0.005663</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.342270</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2562 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                     t1      trgt  side  ...  bin         p  pred\n",
              "date_time                                                ...                     \n",
              "2015-07-09 09:56:21 2015-07-10 09:56:51  0.008702  -1.0  ...  0.0  0.289559   0.0\n",
              "2015-07-09 12:00:31 2015-07-10 12:27:37  0.007981  -1.0  ...  0.0  0.413559   0.0\n",
              "2015-07-09 15:59:28 2015-07-10 09:35:27  0.006833   1.0  ...  1.0  0.253173   0.0\n",
              "2015-07-10 09:35:27 2015-07-13 09:30:23  0.006412   1.0  ...  1.0  0.361351   0.0\n",
              "2015-07-10 11:48:00 2015-07-13 09:30:23  0.005870   1.0  ...  1.0  0.254544   0.0\n",
              "...                                 ...       ...   ...  ...  ...       ...   ...\n",
              "2019-06-12 11:59:18 2019-06-13 12:16:14  0.005909  -1.0  ...  0.0  0.286559   0.0\n",
              "2019-06-13 09:36:29 2019-06-14 09:48:52  0.005255  -1.0  ...  0.0  0.369559   0.0\n",
              "2019-06-18 09:50:22 2019-06-19 09:57:29  0.005684  -1.0  ...  0.0  0.229559   0.0\n",
              "2019-06-18 12:22:51 2019-06-19 12:33:36  0.007223   1.0  ...  0.0  0.289544   0.0\n",
              "2019-06-19 14:01:58 2019-06-20 09:30:00  0.005663   1.0  ...  1.0  0.342270   0.0\n",
              "\n",
              "[2562 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R8oXfxfc8fvD",
        "colab_type": "text"
      },
      "source": [
        "### bet size probability"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n2ei_4N38huh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "m_prob = bs.bet_size_probability(events = bs_df[['t1']], prob = bs_df.p, num_classes=2, num_threads=3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HMjvm6oW87-v",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "db4fcd71-39fc-4abb-84d4-2d052683d370"
      },
      "source": [
        "m_prob"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date_time\n",
              "2015-07-09 09:56:21   -0.357336\n",
              "2015-07-09 12:00:31   -0.139332\n",
              "2015-07-09 15:59:28   -0.429721\n",
              "2015-07-10 09:35:27   -0.227126\n",
              "2015-07-10 11:48:00   -0.426895\n",
              "                         ...   \n",
              "2019-06-12 11:59:18   -0.363111\n",
              "2019-06-13 09:36:29   -0.213024\n",
              "2019-06-18 09:50:22   -0.479818\n",
              "2019-06-18 12:22:51   -0.357366\n",
              "2019-06-19 14:01:58   -0.260439\n",
              "Name: signal, Length: 2562, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KvmeDg5u9Qhl",
        "colab_type": "text"
      },
      "source": [
        "average"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CWBHmphD9Tdq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "d81212d5-4ba1-4ac2-b9c3-e0d42cca3254"
      },
      "source": [
        "m_prob_avg = bs.bet_size_probability(events = bs_df[['t1']], prob = bs_df.p, num_classes=2, num_threads=3, average_active=True)\n",
        "m_prob_avg"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-01-24 09:50:25.471265 100.0% mp_avg_active_signals done after 0.17 minutes. Remaining 0.0 minutes.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2015-07-09 09:56:21   -0.357336\n",
              "2015-07-09 12:00:31   -0.248334\n",
              "2015-07-09 15:59:28   -0.308797\n",
              "2015-07-10 09:35:27   -0.241265\n",
              "2015-07-10 09:56:51   -0.183229\n",
              "                         ...   \n",
              "2019-06-18 12:22:51   -0.418592\n",
              "2019-06-19 09:57:29   -0.357366\n",
              "2019-06-19 12:33:36    0.000000\n",
              "2019-06-19 14:01:58   -0.260439\n",
              "2019-06-20 09:30:00    0.000000\n",
              "Length: 3830, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AArwEUGZ9USQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "d381a3ad-8ae8-47e9-8a72-ae8476bf5eb0"
      },
      "source": [
        "m_prob_discrete = bs.bet_size_probability(events = bs_df[['t1']], prob = bs_df.p, num_classes=2, num_threads=3, average_active=True, step_size=0.01)\n",
        "m_prob_discrete"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-01-24 09:51:11.092596 100.0% mp_avg_active_signals done after 0.17 minutes. Remaining 0.0 minutes.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2015-07-09 09:56:21   -0.36\n",
              "2015-07-09 12:00:31   -0.25\n",
              "2015-07-09 15:59:28   -0.31\n",
              "2015-07-10 09:35:27   -0.24\n",
              "2015-07-10 09:56:51   -0.18\n",
              "                       ... \n",
              "2019-06-18 12:22:51   -0.42\n",
              "2019-06-19 09:57:29   -0.36\n",
              "2019-06-19 12:33:36    0.00\n",
              "2019-06-19 14:01:58   -0.26\n",
              "2019-06-20 09:30:00    0.00\n",
              "Length: 3830, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Islsd4M729B",
        "colab_type": "text"
      },
      "source": [
        "### bet size reserve"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dIgIVhFu70XZ",
        "colab_type": "code",
        "outputId": "1db3fb26-6126-4144-a219-b4ab0e969fb8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "m_reserve = bs.bet_sizing.bet_size_reserve(events_t1=bs_df['t1'], sides=bs_df.side, return_parameters=True, fit_runs=50)"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "|#########################| Completed 100 of 100 fitting rounds."
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D7T72qjx75c5",
        "colab_type": "code",
        "outputId": "8546c002-94d2-4cce-fa47-5bda08a9dfcb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        }
      },
      "source": [
        "m_reserve[0]"
      ],
      "execution_count": 174,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>2015-07-09 09:56:21</th>\n",
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              "      <td>2015-07-10 12:27:37</td>\n",
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              "      <th>2015-07-10 09:35:27</th>\n",
              "      <td>2015-07-13 09:30:23</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>-1</td>\n",
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              "      <th>2015-07-10 11:48:00</th>\n",
              "      <td>2015-07-13 09:30:23</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>2019-06-18 09:50:22</th>\n",
              "      <td>2019-06-19 09:57:29</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-19 14:01:58</th>\n",
              "      <td>2019-06-20 09:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.213839</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2562 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                     t1  side  ...  c_t  bet_size\n",
              "date_time                                      ...               \n",
              "2015-07-09 09:56:21 2015-07-10 09:56:51  -1.0  ...   -1 -0.151880\n",
              "2015-07-09 12:00:31 2015-07-10 12:27:37  -1.0  ...   -2 -0.305209\n",
              "2015-07-09 15:59:28 2015-07-10 09:35:27   1.0  ...   -1 -0.151880\n",
              "2015-07-10 09:35:27 2015-07-13 09:30:23   1.0  ...   -1 -0.151880\n",
              "2015-07-10 11:48:00 2015-07-13 09:30:23   1.0  ...    1  0.213839\n",
              "...                                 ...   ...  ...  ...       ...\n",
              "2019-06-12 11:59:18 2019-06-13 12:16:14  -1.0  ...    2  0.405896\n",
              "2019-06-13 09:36:29 2019-06-14 09:48:52  -1.0  ...   -2 -0.305209\n",
              "2019-06-18 09:50:22 2019-06-19 09:57:29  -1.0  ...   -1 -0.151880\n",
              "2019-06-18 12:22:51 2019-06-19 12:33:36   1.0  ...    0  0.000000\n",
              "2019-06-19 14:01:58 2019-06-20 09:30:00   1.0  ...    1  0.213839\n",
              "\n",
              "[2562 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m6vfUje3CBPE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "BetSizeReserve = m_reserve[0].bet_size"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NucjJ_DN9vl_",
        "colab_type": "code",
        "outputId": "a06c8bad-5b18-4584-a450-4f518e816696",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "m_reserve[1]\n",
        "#parameter for two Gaussian mixture"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mu_1': -1.44814,\n",
              " 'mu_2': 3.71211,\n",
              " 'p_1': 0.92838,\n",
              " 'sigma_1': 4.10757,\n",
              " 'sigma_2': 2.85336}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S2NP_MKf_5s7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "99a3ee35-ec5c-46e0-f8ab-0ea2e6128586"
      },
      "source": [
        "m_budget = bs.bet_sizing.bet_size_budget(bs_df.t1, bs_df.side)\n",
        "m_budget"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>t1</th>\n",
              "      <th>side</th>\n",
              "      <th>active_long</th>\n",
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              "      <th>bet_size</th>\n",
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              "    <tr>\n",
              "      <th>date_time</th>\n",
              "      <th></th>\n",
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              "      <th>2015-07-09 09:56:21</th>\n",
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              "      <td>-1.0</td>\n",
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              "      <th>2015-07-09 12:00:31</th>\n",
              "      <td>2015-07-10 12:27:37</td>\n",
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              "      <th>2015-07-09 15:59:28</th>\n",
              "      <td>2015-07-10 09:35:27</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>2015-07-10 09:35:27</th>\n",
              "      <td>2015-07-13 09:30:23</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>2015-07-10 11:48:00</th>\n",
              "      <td>2015-07-13 09:30:23</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2019-06-12 11:59:18</th>\n",
              "      <td>2019-06-13 12:16:14</td>\n",
              "      <td>-1.0</td>\n",
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              "      <td>1</td>\n",
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              "      <th>2019-06-13 09:36:29</th>\n",
              "      <td>2019-06-14 09:48:52</td>\n",
              "      <td>-1.0</td>\n",
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              "    <tr>\n",
              "      <th>2019-06-18 09:50:22</th>\n",
              "      <td>2019-06-19 09:57:29</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.035714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-18 12:22:51</th>\n",
              "      <td>2019-06-19 12:33:36</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.035714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-19 14:01:58</th>\n",
              "      <td>2019-06-20 09:30:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0.071429</td>\n",
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              "<p>2562 rows × 5 columns</p>\n",
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            ],
            "text/plain": [
              "                                     t1  side  ...  active_short  bet_size\n",
              "date_time                                      ...                        \n",
              "2015-07-09 09:56:21 2015-07-10 09:56:51  -1.0  ...             1 -0.035714\n",
              "2015-07-09 12:00:31 2015-07-10 12:27:37  -1.0  ...             2 -0.071429\n",
              "2015-07-09 15:59:28 2015-07-10 09:35:27   1.0  ...             2  0.000000\n",
              "2015-07-10 09:35:27 2015-07-13 09:30:23   1.0  ...             2  0.000000\n",
              "2015-07-10 11:48:00 2015-07-13 09:30:23   1.0  ...             1  0.107143\n",
              "...                                 ...   ...  ...           ...       ...\n",
              "2019-06-12 11:59:18 2019-06-13 12:16:14  -1.0  ...             1  0.178571\n",
              "2019-06-13 09:36:29 2019-06-14 09:48:52  -1.0  ...             2 -0.071429\n",
              "2019-06-18 09:50:22 2019-06-19 09:57:29  -1.0  ...             1 -0.035714\n",
              "2019-06-18 12:22:51 2019-06-19 12:33:36   1.0  ...             1  0.035714\n",
              "2019-06-19 14:01:58 2019-06-20 09:30:00   1.0  ...             0  0.071429\n",
              "\n",
              "[2562 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "04NQKAnVBjKt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "BetSizeBudget = m_budget.bet_size"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lAjfISA7_qvn",
        "colab_type": "text"
      },
      "source": [
        "overview"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F_2osvUt941C",
        "colab_type": "code",
        "outputId": "24ae9aba-99f1-45ca-fdfe-72bb20e9ffb7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        }
      },
      "source": [
        "bet_size_0 = pd.DataFrame({'BetSizeProb':m_prob,'BetSizeProbAvg':m_prob_avg,'BetSizeProbDiscrete':m_prob_discrete})\n",
        "bet_size_1 = bet_size_0.join(BetSizeReserve)\n",
        "bet_size = bet_size_1.join(BetSizeBudget,rsuffix='2')\n",
        "bet_size = bet_size.rename(columns={'bet_size':'BetSizeReserve', 'bet_size2':'BetSizeBudget'})\n",
        "bet_size"
      ],
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BetSizeProb</th>\n",
              "      <th>BetSizeProbAvg</th>\n",
              "      <th>BetSizeProbDiscrete</th>\n",
              "      <th>BetSizeReserve</th>\n",
              "      <th>BetSizeBudget</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015-07-09 09:56:21</th>\n",
              "      <td>-0.357336</td>\n",
              "      <td>-0.357336</td>\n",
              "      <td>-0.36</td>\n",
              "      <td>-0.151880</td>\n",
              "      <td>-0.035714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-09 12:00:31</th>\n",
              "      <td>-0.139332</td>\n",
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              "    <tr>\n",
              "      <th>2015-07-09 15:59:28</th>\n",
              "      <td>-0.429721</td>\n",
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              "    <tr>\n",
              "      <th>2015-07-10 09:35:27</th>\n",
              "      <td>-0.227126</td>\n",
              "      <td>-0.241265</td>\n",
              "      <td>-0.24</td>\n",
              "      <td>-0.151880</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-10 09:56:51</th>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.183229</td>\n",
              "      <td>-0.18</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-18 12:22:51</th>\n",
              "      <td>-0.357366</td>\n",
              "      <td>-0.418592</td>\n",
              "      <td>-0.42</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.035714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-19 09:57:29</th>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.357366</td>\n",
              "      <td>-0.36</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-19 12:33:36</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-19 14:01:58</th>\n",
              "      <td>-0.260439</td>\n",
              "      <td>-0.260439</td>\n",
              "      <td>-0.26</td>\n",
              "      <td>0.213839</td>\n",
              "      <td>0.071429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-20 09:30:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3830 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                     BetSizeProb  BetSizeProbAvg  ...  BetSizeReserve  BetSizeBudget\n",
              "2015-07-09 09:56:21    -0.357336       -0.357336  ...       -0.151880      -0.035714\n",
              "2015-07-09 12:00:31    -0.139332       -0.248334  ...       -0.305209      -0.071429\n",
              "2015-07-09 15:59:28    -0.429721       -0.308797  ...       -0.151880       0.000000\n",
              "2015-07-10 09:35:27    -0.227126       -0.241265  ...       -0.151880       0.000000\n",
              "2015-07-10 09:56:51          NaN       -0.183229  ...             NaN            NaN\n",
              "...                          ...             ...  ...             ...            ...\n",
              "2019-06-18 12:22:51    -0.357366       -0.418592  ...        0.000000       0.035714\n",
              "2019-06-19 09:57:29          NaN       -0.357366  ...             NaN            NaN\n",
              "2019-06-19 12:33:36          NaN        0.000000  ...             NaN            NaN\n",
              "2019-06-19 14:01:58    -0.260439       -0.260439  ...        0.213839       0.071429\n",
              "2019-06-20 09:30:00          NaN        0.000000  ...             NaN            NaN\n",
              "\n",
              "[3830 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lSK9HdDhRJIB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bet_size.dropna(inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tzyV4XujNPeK",
        "colab_type": "text"
      },
      "source": [
        "Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JLUEVfuhKMbc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "outputId": "96ac4054-db97-4c73-b2fc-cbc3015e89b9"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(10,6))\n",
        "bet_size_3 = bet_size.drop(['BetSizeProbAvg','BetSizeProbDiscrete'],axis=1)\n",
        "ax.plot(bet_size_3[-100:])\n",
        "\n",
        "ax.legend(labels=bet_size_3.columns,loc='upper left', fontsize=12, title=\"Bet size type\", title_fontsize=10)\n",
        "ax.set_title(\"Bet Size 100 points\", fontsize=14)"
      ],
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Bet Size 100 points')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 216
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lr3Xw4K+8886bbNmy3bYtKyuLqKhI9u/fi8FgwNfXjwcffIjhw0cC1uWZblZ4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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yaSu4zBICuFN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "outputId": "00e83c72-783e-4924-a0eb-1108506a1277"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(10,6))\n",
        "bet_size_4 = bet_size.iloc[:,:3]\n",
        "ax.plot(bet_size_4[-100:])\n",
        "\n",
        "ax.legend(labels=bet_size_4.columns,loc='upper left', fontsize=12, title=\"Bet size type\", title_fontsize=10)\n",
        "ax.set_title(\"Bet Size Prob 100 points\", fontsize=14)"
      ],
      "execution_count": 217,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Bet Size Prob 100 points')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 217
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wcjBwYD9SU1PRaNRoNE60atWacePevuF+iIcPH2Lq1Els2vST9bnMzEwWLpzH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\nRgghhBASZxSAEUIIIYTE2f8PxNyK75E6Z1AAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1LRuhbKtNx2N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "outputId": "5e86ac72-22bb-49bc-cb41-3a47a1c4f1c3"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(10,6))\n",
        "for c in ['BetSizeProb','BetSizeReserve', 'BetSizeBudget']:\n",
        "    ax.hist(bet_size_3[c],  label=c, alpha=0.6, bins=100)\n",
        "ax.legend(loc='upper left', fontsize=12, title=\"Bet size type\", title_fontsize=10)\n",
        "ax.set_xlabel(\"Bet Size, $m_t$\", fontsize=12)\n",
        "ax.set_ylabel(\"Value count\", fontsize=12)\n",
        "ax.set_title(\"Bet Size Distribution\", fontsize=14)"
      ],
      "execution_count": 222,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Bet Size Distribution')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 222
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SqlUsAKNHx2M2m0lKOsb27dvo1q0nn366mOXLVzuTtu+++xdz587ko4+WYLfb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\niIjIwP4fqxFXiVWfEhIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0LAHHJkNaa1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "outputId": "dce01832-1a4d-4ef7-c753-8fc2982bef04"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(10,6))\n",
        "for c in ['BetSizeProb','BetSizeProbAvg', 'BetSizeProbDiscrete']:\n",
        "    ax.hist(bet_size_4[c],  label=c, alpha=0.6, bins=100)\n",
        "ax.legend(loc='upper left', fontsize=12, title=\"Bet size type\", title_fontsize=10)\n",
        "ax.set_xlabel(\"Bet Size, $m_t$\", fontsize=12)\n",
        "ax.set_ylabel(\"Value count\", fontsize=12)\n",
        "ax.set_title(\"Bet Size Distribution\", fontsize=14)"
      ],
      "execution_count": 223,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Bet Size Distribution')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 223
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fuHE93bu/wpo1m6hatRqjRg1zOx5bt26hTZt2tGnTjs2bN5KVlQXAY4815fDh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\nrBERERFZ2P8DOcN91dGzY/EAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NK_HVmubO9hn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d7YIGyDtPH92",
        "colab_type": "text"
      },
      "source": [
        "## REFERENCE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ym6rl-oGPJIT",
        "colab_type": "text"
      },
      "source": [
        "Advances in Financial Machine Learning, Lopez de Prado\n",
        "\n",
        "https://github.com/hudson-and-thames/research"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPcMO2z3PUCY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}